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Link to original content: https://unpaywall.org/10.1007/S11432-020-3128-2
Distributed event triggering control for six-rotor UAV systems with asymmetric time-varying output constraints | Science China Information Sciences Skip to main content
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Distributed event triggering control for six-rotor UAV systems with asymmetric time-varying output constraints

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Abstract

Inspired by the practical operability and safety of unmanned aerial vehicles (UAVs) in confined areas, this paper investigates adaptive trajectory tracking control problems in multiple six-rotor UAV systems with asymmetric time-varying output constraints and input saturation. Under model and disturbance uncertainties, six-rotor UAV systems are modeled as two non-strict-feedback systems, including attitude (inner-loop) and position (outer-loop) regulation systems. For the inner-loop design, the neural-based distributed adaptive attitude consensus control protocol is employed to realize the leader-follower consensus. Adaptive first-order sliding mode differentiators and an auxiliary dynamic system are introduced to address the “explosion of complexity” and saturation nonlinearity issues, respectively. Then, an event-triggered condition is predefined to alleviate the communication loads and reduce the number of messages to be transmitted from the controller to actuator. In addition, a class of asymmetric time-varying barrier Lyapunov functions are constructed for preventing the violation of time-varying output constraints. Accordingly, the proposed double-loop control strategies guarantee that all signals of UAV systems are semi-globally and uniformly bounded. Simulation results demonstrate that the proposed control method is effective.

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References

  1. Cai G W, Chen B M, Peng K, et al. Modeling and control of the yaw channel of a UAV helicopter. IEEE Trans Ind Electron, 2008, 55: 3426–3434

    Article  Google Scholar 

  2. Lin F, Dong X X, Chen B M, et al. A robust real-time embedded vision system on an unmanned rotorcraft for ground target following. IEEE Trans Ind Electron, 2012, 59: 1038–1049

    Article  Google Scholar 

  3. Duan H B, Xin L, Chen S J. Robust cooperative target detection for a vision-based UAVs autonomous aerial refueling platform via the contrast sensitivity mechanism of eagle’s eye. IEEE Aerosp Electron Syst Mag, 2019, 34: 18–30

    Article  Google Scholar 

  4. Zuo Z Y, Wang C. Adaptive trajectory tracking control of output constrained multi-rotors systems. IET Control Theor Appl, 2014, 8: 1163–1174

    Article  Google Scholar 

  5. Tian B, Liu L, Lu H, et al. Multivariable finite time attitude control for quadrotor UAV: theory and experimentation. IEEE Trans Ind Electron, 2018, 65: 2567–2577

    Article  Google Scholar 

  6. Zhang Z, Wang F, Guo Y, et al. Multivariable sliding mode backstepping controller design for quadrotor UAV based on disturbance observer. Sci China Inf Sci, 2018, 61: 112207

    Article  Google Scholar 

  7. Zuo Z Y. Adaptive trajectory tracking control design with command filtered compensation for a quadrotor. J Vib Control, 2013, 19: 94–108

    Article  MathSciNet  MATH  Google Scholar 

  8. Islam S, Liu P X, Saddik A E. Robust control of four-rotor unmanned aerial vehicle with disturbance uncertainty. IEEE Trans Ind Electron, 2015, 62: 1563–1571

    Article  Google Scholar 

  9. He T P, Liu H, Li S. Quaternion-based robust trajectory tracking control for uncertain quadrotors. Sci China Inf Sci, 2016, 59: 122902

    Article  Google Scholar 

  10. Xiao B, Yin S. A new disturbance attenuation control scheme for quadrotor unmanned aerial vehicles. IEEE Trans Ind Inf, 2017, 13: 2922–2932

    Article  Google Scholar 

  11. He W, Ge S S. Vibration control of a flexible string with both boundary input and output constraints. IEEE Trans Contr Syst Technol, 2015, 23: 1245–1254

    Article  Google Scholar 

  12. Liu Y J, Tong S. Barrier Lyapunov functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints. Automatica, 2016, 64: 70–75

    Article  MathSciNet  MATH  Google Scholar 

  13. Lv M, Yu W, Baldi S. The set-invariance paradigm in fuzzy adaptive DSC design of large-scale nonlinear input-constrained systems. IEEE Trans Syst Man Cybern Syst, 2021, 51: 1035–1045

    Article  Google Scholar 

  14. Edalati L, Sedigh A K, Shooredeli M A, et al. Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints. Mech Syst Signal Process, 2018, 100: 311–329

    Article  Google Scholar 

  15. Gilbert E, Kolmanovsky I. Nonlinear tracking control in the presence of state and control constraints: a generalized reference governor. Automatica, 2002, 38: 2063–2073

    Article  MathSciNet  MATH  Google Scholar 

  16. Zhang M, Jing X. A bioinspired dynamics-based adaptive fuzzy SMC method for half-car active suspension systems with input dead zones and saturations. IEEE Trans Cybern, 2021, 51: 1743–1755

    Article  Google Scholar 

  17. Tee K P, Ren B, Ge S S. Control of nonlinear systems with time-varying output constraints. Automatica, 2011, 47: 2511–2516

    Article  MathSciNet  MATH  Google Scholar 

  18. Fu C, Hong W, Lu H, et al. Adaptive robust backstepping attitude control for a multi-rotor unmanned aerial vehicle with time-varying output constraints. Aerospace Sci Tech, 2018, 78: 593–603

    Article  Google Scholar 

  19. Kuriki Y, Namerikawa T. Consensus-based cooperative formation control with collision avoidance for a multi-UAV system. In: Proceedings of American Control Conference, 2014. 2077–2082

  20. Dong X, Hua Y, Zhou Y, et al. Theory and experiment on formation-containment control of multiple multirotor unmanned aerial vehicle systems. IEEE Trans Automat Sci Eng, 2019, 16: 229–240

    Article  Google Scholar 

  21. Zou Y, Zhou Z, Dong X, et al. Distributed formation control for multiple vertical takeoff and landing UAVs with switching topologies. IEEE/ASME Trans Mechatron, 2018, 23: 1750–1761

    Article  Google Scholar 

  22. Wang J N, Zhou Z Y, Wang C Y, et al. Cascade structure predictive observer design for consensus control with applications to UAVs formation flying. Automatica, 2020, 121: 109200

    Article  MathSciNet  MATH  Google Scholar 

  23. Zhu S Y, Liu Y, Lou Y J, et al. Stabilization of logical control networks: an event-triggered control approach. Sci China Inf Sci, 2020, 63: 112203

    Article  MathSciNet  Google Scholar 

  24. Chen Z Y, Han Q-L, Yan Y M, et al. How often should one update control and estimation: review of networked triggering techniques. Sci China Inf Sci, 2020, 63: 150201

    Article  MathSciNet  Google Scholar 

  25. Zhu W, Wang D D, Zhou Q H. Leader-following consensus of multi-agent systems via adaptive event-based control. J Syst Sci Complex, 2019, 32: 846–856

    Article  MathSciNet  MATH  Google Scholar 

  26. Su Y, Wang Q, Sun C. Self-triggered consensus control for linear multi-agent systems with input saturation. IEEE/CAA J Autom Sin, 2020, 7: 150–157

    Article  MathSciNet  Google Scholar 

  27. Yang B, Zhou Q, Cao L, et al. Event-triggered control for multi-agent systems with prescribed performance and full state constraints (in Chinese). Acta Autom Sin, 2019, 45: 1527–1535

    MATH  Google Scholar 

  28. Yao D, Li H, Lu R, et al. Distributed sliding-mode tracking control of second-order nonlinear multiagent systems: an event-triggered approach. IEEE Trans Cybern, 2020, 50: 3892–3902

    Article  Google Scholar 

  29. Ma H, Li H Y, Lu R Q, et al. Adaptive event-triggered control for a class of nonlinear systems with periodic disturbances. Sci China Inf Sci, 2020, 63: 150212

    Article  MathSciNet  Google Scholar 

  30. Zhang H, Chen J, Wang Z P, et al. Distributed event-triggered control for cooperative output regulation of multiagent systems with an online estimation algorithm. IEEE Trans Cybern, 2020. doi: https://doi.org/10.1109/TCYB.2020.2991761

  31. Liang H, Liu G, Zhang H, et al. Neural-network-based event-triggered adaptive control of nonaffine nonlinear multiagent systems with dynamic uncertainties. IEEE Trans Neural Netw Learning Syst, 2021, 32: 2239–2250

    Article  MathSciNet  Google Scholar 

  32. Bai W W, Chen G D, Zhou Q, et al. Disturbance-observer-based event-triggered control for multi-agent systems with input saturation (in Chinese). Sci Sin Inform, 2019, 49: 1502–1516

    Article  Google Scholar 

  33. Chen F, Jiang R, Zhang K, et al. Robust backstepping sliding mode control and observer-based fault estimation for a quadrotor UAV. IEEE Trans Ind Electron, 2016, 63: 5044–5056

    Google Scholar 

  34. Xu B, Shi Z K, Sun F C, et al. Barrier Lyapunov function based learning control of hypersonic flight vehicle with AOA constraint and actuator faults. IEEE Trans Cybern, 2019, 49: 1047–1057

    Article  Google Scholar 

  35. Chen J, Kai S X. Cooperative transportation control of multiple mobile manipulators through distributed optimization. Sci China Inf Sci, 2018, 61: 120201

    Article  MathSciNet  Google Scholar 

  36. Xu X, Liu L, Feng G. Consensus of single integrator multi-agent systems with unbounded transmission delays. J Syst Sci Complex, 2019, 32: 778–788

    Article  MathSciNet  MATH  Google Scholar 

  37. Li Z, Gao L, Chen W, et al. Distributed adaptive cooperative tracking of uncertain nonlinear fractional-order multi-agent systems. IEEE/CAA J Autom Sin, 2020, 7: 292–300

    Article  MathSciNet  Google Scholar 

  38. Lin G H, Li H Y, Ma H, et al. Human-in-the-loop consensus control for nonlinear multi-agent systems with actuator faults. IEEE/CAA J Autom Sin, 2020. doi: https://doi.org/10.1109/JAS.2020.1003596

  39. Li H Y, Wu Y, Chen M. Adaptive fault-tolerant tracking control for discrete-time multi-agent systems via reinforcement learning algorithm. IEEE Trans Cybern, 2021, 51: 1163–1174

    Article  Google Scholar 

  40. Zhou Q, Zhao S, Li H, et al. Adaptive neural network tracking control for robotic manipulators with dead zone. IEEE Trans Neural Netw Learn Syst, 2019, 30: 3611–3620

    Article  MathSciNet  Google Scholar 

  41. Xu B, Yang D P, Shi Z K, et al. Online recorded data-based composite neural control of strict-feedback systems with application to hypersonic flight dynamics. IEEE Trans Neural Netw Learn Syst, 2018, 29: 3839–3849

    Article  MathSciNet  Google Scholar 

  42. Bai W W, Li T S, Tong S C. NN reinforcement learning adaptive control for a class of nonstrict-feedback discrete-time systems. IEEE Trans Cybern, 2020, 50: 4573–4584

    Article  Google Scholar 

  43. Wang F, Chen B, Lin C, et al. Distributed adaptive neural control for stochastic nonlinear multiagent systems. IEEE Trans Cybern, 2017, 47: 1795–1803

    Article  Google Scholar 

  44. Xi C, Dong J. Event-triggered adaptive fuzzy distributed tracking control for uncertain nonlinear multi-agent systems. Fuzzy Sets Syst, 2021, 402: 35–50

    Article  MathSciNet  MATH  Google Scholar 

  45. Liu Y, Liu X, Jing Y, et al. Annular domain finite-time connective control for large-scale systems with expanding construction. IEEE Trans Syst Man Cybern Syst, 2020. doi: https://doi.org/10.1109/TSMC.2019.2960009

  46. Pan Y, Du P, Xue H, et al. Singularity-free fixed-time fuzzy control for robotic systems with user-defined performance. IEEE Trans Fuzzy Syst, 2020. doi: https://doi.org/10.1109/TFUZZ.2020.2999746

  47. Zhang C K, Long F, He Y, et al. A relaxed quadratic function negative-determination lemma and its application to time-delay systems. Automatica, 2020, 113: 108764

    Article  MathSciNet  MATH  Google Scholar 

  48. Long F, Jiang L, He Y, et al. Stability analysis of systems with time-varying delay via novel augmented Lyapunov-Krasovskii functionals and an improved integral inequality. Appl Math Comput, 2019, 357: 325–337

    MathSciNet  MATH  Google Scholar 

  49. Zhang H, Wang J. Active steering actuator fault detection for an automatically-steered electric ground vehicle. IEEE Trans Veh Technol, 2017, 66: 3685–3702

    Google Scholar 

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Grant Nos. 62033003, 62003093, 61803105, U1911401), Local Innovative and Research Teams Project of Guangdong Special Support Program (Grant No. 2019BT02X353), China Postdoctoral Science Foundation (Grant No. 2020M682614), and Science and Technology Program of Guangzhou (Grant No. 201904020006).

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Authors and Affiliations

  1. Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou, 510006, China

    Liang Cao, Hongru Ren, Wei Meng, Hongyi Li & Renquan Lu

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  1. Liang Cao
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  2. Hongru Ren
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  3. Wei Meng
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  4. Hongyi Li
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Correspondence to Hongru Ren.

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Cao, L., Ren, H., Meng, W. et al. Distributed event triggering control for six-rotor UAV systems with asymmetric time-varying output constraints. Sci. China Inf. Sci. 64, 172213 (2021). https://doi.org/10.1007/s11432-020-3128-2

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  • DOI: https://doi.org/10.1007/s11432-020-3128-2

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